






from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__   :      int				=		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   :      Any				=		{
    """microsoft/cvt-13""": """https://huggingface.co/microsoft/cvt-13/resolve/main/config.json""",
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}
class     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	'''cvt'''







				def __init__(    self							,       __lowerCAmelCase=3							,       __lowerCAmelCase=[7, 3, 3]							,       __lowerCAmelCase=[4, 2, 2]							,       __lowerCAmelCase=[2, 1, 1]							,       __lowerCAmelCase=[6_4, 1_9_2, 3_8_4]							,       __lowerCAmelCase=[1, 3, 6]							,       __lowerCAmelCase=[1, 2, 1_0]							,       __lowerCAmelCase=[4.0, 4.0, 4.0]							,       __lowerCAmelCase=[0.0, 0.0, 0.0]							,       __lowerCAmelCase=[0.0, 0.0, 0.0]							,       __lowerCAmelCase=[0.0, 0.0, 0.1]							,       __lowerCAmelCase=[True, True, True]							,       __lowerCAmelCase=[False, False, True]							,       __lowerCAmelCase=["dw_bn", "dw_bn", "dw_bn"]							,       __lowerCAmelCase=[3, 3, 3]							,       __lowerCAmelCase=[1, 1, 1]							,       __lowerCAmelCase=[2, 2, 2]							,       __lowerCAmelCase=[1, 1, 1]							,       __lowerCAmelCase=[1, 1, 1]							,       __lowerCAmelCase=0.02							,       __lowerCAmelCase=1E-12							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								super().__init__(**__lowerCAmelCase  )
								__magic_name__		:Optional[int]   =   num_channels
								__magic_name__		:Optional[int]   =   patch_sizes
								__magic_name__		:Optional[Any]   =   patch_stride
								__magic_name__		:int   =   patch_padding
								__magic_name__		:List[str]   =   embed_dim
								__magic_name__		:Tuple   =   num_heads
								__magic_name__		:Dict   =   depth
								__magic_name__		:Optional[Any]   =   mlp_ratio
								__magic_name__		:Optional[int]   =   attention_drop_rate
								__magic_name__		:Any   =   drop_rate
								__magic_name__		:Tuple   =   drop_path_rate
								__magic_name__		:Union[str, Any]   =   qkv_bias
								__magic_name__		:Tuple   =   cls_token
								__magic_name__		:Union[str, Any]   =   qkv_projection_method
								__magic_name__		:Tuple   =   kernel_qkv
								__magic_name__		:Optional[int]   =   padding_kv
								__magic_name__		:Dict   =   stride_kv
								__magic_name__		:int   =   padding_q
								__magic_name__		:List[Any]   =   stride_q
								__magic_name__		:Union[str, Any]   =   initializer_range
								__magic_name__		:Optional[Any]   =   layer_norm_eps












'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase_     						=				{
    '''configuration_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_     						=				[
        '''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''RobertaPreLayerNormForCausalLM''',
        '''RobertaPreLayerNormForMaskedLM''',
        '''RobertaPreLayerNormForMultipleChoice''',
        '''RobertaPreLayerNormForQuestionAnswering''',
        '''RobertaPreLayerNormForSequenceClassification''',
        '''RobertaPreLayerNormForTokenClassification''',
        '''RobertaPreLayerNormModel''',
        '''RobertaPreLayerNormPreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_     						=				[
        '''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFRobertaPreLayerNormForCausalLM''',
        '''TFRobertaPreLayerNormForMaskedLM''',
        '''TFRobertaPreLayerNormForMultipleChoice''',
        '''TFRobertaPreLayerNormForQuestionAnswering''',
        '''TFRobertaPreLayerNormForSequenceClassification''',
        '''TFRobertaPreLayerNormForTokenClassification''',
        '''TFRobertaPreLayerNormMainLayer''',
        '''TFRobertaPreLayerNormModel''',
        '''TFRobertaPreLayerNormPreTrainedModel''',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_     						=				[
        '''FlaxRobertaPreLayerNormForCausalLM''',
        '''FlaxRobertaPreLayerNormForMaskedLM''',
        '''FlaxRobertaPreLayerNormForMultipleChoice''',
        '''FlaxRobertaPreLayerNormForQuestionAnswering''',
        '''FlaxRobertaPreLayerNormForSequenceClassification''',
        '''FlaxRobertaPreLayerNormForTokenClassification''',
        '''FlaxRobertaPreLayerNormModel''',
        '''FlaxRobertaPreLayerNormPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_roberta_prelayernorm import (
        ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
        RobertaPreLayerNormConfig,
        RobertaPreLayerNormOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_roberta_prelayernorm import (
            ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            RobertaPreLayerNormForCausalLM,
            RobertaPreLayerNormForMaskedLM,
            RobertaPreLayerNormForMultipleChoice,
            RobertaPreLayerNormForQuestionAnswering,
            RobertaPreLayerNormForSequenceClassification,
            RobertaPreLayerNormForTokenClassification,
            RobertaPreLayerNormModel,
            RobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_roberta_prelayernorm import (
            TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRobertaPreLayerNormForCausalLM,
            TFRobertaPreLayerNormForMaskedLM,
            TFRobertaPreLayerNormForMultipleChoice,
            TFRobertaPreLayerNormForQuestionAnswering,
            TFRobertaPreLayerNormForSequenceClassification,
            TFRobertaPreLayerNormForTokenClassification,
            TFRobertaPreLayerNormMainLayer,
            TFRobertaPreLayerNormModel,
            TFRobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_roberta_prelayernorm import (
            FlaxRobertaPreLayerNormForCausalLM,
            FlaxRobertaPreLayerNormForMaskedLM,
            FlaxRobertaPreLayerNormForMultipleChoice,
            FlaxRobertaPreLayerNormForQuestionAnswering,
            FlaxRobertaPreLayerNormForSequenceClassification,
            FlaxRobertaPreLayerNormForTokenClassification,
            FlaxRobertaPreLayerNormModel,
            FlaxRobertaPreLayerNormPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase_     						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


